Search

The efficient use of energy is a critical aspect in Wireless Sensor Networks (WSNs) to prolong the network's lifespan. Investigating routing in WSNs requires considering the effective establishment of chains or clustering methods to optimize the network. The proposed routing technique aims to enhance sensor longevity through different network partitioning methods. The strategy employs the PEGASIS (Power Efficient Gathering in Sensor Information Systems) protocol, which uses Prim's Algorithm to adjust the chain structure and is based on hierarchical chain-based routing. To transmit data from working nodes to the base station (BS), horizontal network partitioning techniques called EEPEG-PA-H and EEPEG-PA-V are utilized. The transition occurs when a node's remaining energy is low. This method has the potential to significantly increase the average network lifespan compared to existing routing methods. For example, EEPEG-PA-H improves longevity by 22.7763 % compared to PEGASIS and by 1.259 % compared to EEPEG-PA across various network sizes using senor.

The present research and study, aimed to develop a new predictive model that easily navigate to the challenges of risk factors causing a heart stroke and accurately detect the early chances of having the stroke. Knowledge of risk factors impacting in the deterioration of a patient's health in causing the severity of heart stroke, helps future research work in prognosis of heart stoke and implement feature selection techniques by considering all such risk factors. Clustering of binary classes perfectly, without any noise, is a major advantage in predicting heart stroke patients' condition in their early stages of severity. Novel initial centroid selection method FRM is developed which considered the best until date resulting in 100 % clustering of patients into binary classes, which significantly contribute to the enhancement of accuracy results. Major objective is integrating clustering technique with classification algorithms Naïve Bayes, Decision Tree and Artificial Neural Network and built three prediction systems, FRM-NB, FRM-DT, and FRM-ANN performed the best when compared to existing systems, resulting in the best accuracy values in predicting early risk of heart stroke and reducing chances of recurrent heart strokes. We have achieved the best accuracy of 94 % (FRM-NB) and 97 % (FRM-DT), sensitivity of 90 % (FRM-NB) and 95 % (FRM-DT), specificity score of 97 % (FRM-NB) AND 90 % (FRM-DT) and AUC-ROC score of 0.976 (FRM-NB) and 0.953(FRM-DT). FRM-ANN model achieved an accuracy of 98 % with 100 % sensitivity and 0.99 score of AUC, which till date no existing research has achieved.

It is believed that Wuhan is where the SARS-CoV-2 virus first surfaced in 2019. This virus was the cause of the coronavirus epidemic (COVID-19), which was officially declared a worldwide pandemic. To identify and manage COVID-19 while the disease is progressing, a great deal of research has been done. It is difficult to distinguish between COVID-19 and other coronavirus strains due to their genetic similarities. That being said, it is imperative that we quickly determine whether an epidemic is caused by a newly discovered virus or a long-standing illness. This article discusses the DeepCoV deep-learning (DL) deep learning approach, which uses layered convolutional neural networks (CNNs) to classify viral illnesses, including SARS-CoV-2. Numerous SARS-CoV-2-related motifs may also be found by carefully examining the computational filter procedures. By spotting these important patterns, DeepCoV makes CNN transparent. The research was done using the 2019nCoVR datasets, and the findings show that DeepCoV outperformed other benchmark machine learning models in terms of accuracy. DeepCoV also achieved maximum scores of 98.58 % and 98.62 %, respectively, on the receiver operating characteristic curve (AUC-ROC) and precision-recall curve (AUCPR). Furthermore linked to the coronavirus genome are notable patterns, filter activities, and particular feature activation actions. When combined, these results provide important insights into how to apply deep learning (DL) algorithms in lieu of SARS-CoV-2 detection and illness patterns in the SARS-CoV-2 genes.

Academics have become increasingly interested in creating cutting-edge technologies to enhance Intelligent Video Surveillance (IVS) performance in terms of accuracy, speed, complexity, and deployment. It has been noted that precise object detection is the only way for IVS to function well in higher level applications including event interpretation, tracking, classification, and activity recognition. Through the use of cutting-edge techniques, the current study seeks to improve the performance accuracy of object detection techniques based on Gaussian Mixture Models (GMM). It is achieved by developing crucial phases in the object detecting process. In this study, it is discussed how to model each pixel as a mixture of Gaussians and how to update the model using an online k-means approximation. The adaptive mixture model's Gaussian distributions are then analyzed to identify which ones are more likely to be the product of a background process. Each pixel is categorized according to whether the background model is thought to include the Gaussian distribution that best depicts it.

Coronavirus is a disease connected to coronavirus. World Health Organization has declared COVID-19 a pandemic. It has an impact on 212 nations and territories worldwide. Examining and identifying patterns in X-Ray pictures of the lungs is still necessary. Early diagnosis may help to lessen a person's virus exposure and prevent it. Manual diagnosis is a time- and labor-intensive process. Since the COVID-19 virus has the potential to infect individuals all around the world, its finding is extremely concerning. The purpose of this study is to apply machine learning to identify and classify coronaviruses. The COVID-19 is anticipated to be discriminated and categorized in CT-Lung screening and computer-aided diagnosis (CAD). Several machine learning methods, including Decision Tree, Support Vector Machine, K-means clustering, and Radial Basis Function, were utilised in conjunction with clinical samples from patients who had contracted corona. While some medical professionals think an RT-PCR test is the most reliable and economical way to detect Covid-19 patients, others think a lung CT scan is more precise and less expensive. Serum samples, respiratory secretions, and whole blood samples are examples of clinical specimens. As a result of the earlier clinical evaluations, these tissues are used to assess 15 different parameters. As part of the proposed four-phase CAD system, the CT lungs screening collection is followed by a pre-processing step that enhances the appearance of ground-glass opacities (GGOs) nodules, which are initially extremely fuzzy and poorly contrasting due to the absence of contrast. These zones will be found and segmented using a modified K-means technique. Support vector machines (SVM) and radial basis functions (RBF) will be used as the input and target data for machine learning classifiers with a 50x50 pixel resolution to categorise the contaminated zones found during the detection phase (RBF). The 15 input items gathered from clinical specimens may be entered into a graphical user interface (GUI) tool that has been created to help doctors receive accurate findings.

Medical image processing is a rapidly growing and concentrating topic today. Medical image analysis techniques are used to diagnose and cure illnesses. One such fundamental and potentially fatal illness is brain tumor, which is an abnormal growth of brain cells within the brain. Due to the complexity of the brain's anatomy. To improve efficiency and reduce the complexity of the picture segmentation process, this work investigated computer tomography (CT)-based brain tumor segmentation. CT scans are often used to diagnose head traumas, malignancies, and skull fractures. The images from the brain tumor database are evaluated in this study effort, and a preprocessing approach called adaptive median filter is used to increase the image's clarity. The preprocessing stage eliminates noise and high-frequency artifacts from the pictures. The median filter is a type of nonlinear digital filter commonly used to reduce noise in a picture or signal. Regardless of the preprocessing technique used, feature extraction techniques are updated, and then classification procedures such as the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) classifier are applied to the picture to classify it as normal or abnormal. Following classification, aberrant images are tracked and selected for segmentation using the Fuzzy C-Means (FCM) clustering technique and associated optimization techniques. In the suggested technique, centroid optimizations such as Grey Wolf Optimization (GWO) and Social Spider Optimization (SSO) combined with a Genetic Algorithm (GA) are utilized to improve the accuracy of the FCM centroid. The suggested work produces the most extreme execution in tumour picture segmentation evaluation appears differently from other works. The conclusion indicates that the hybrid technique (SSO-GA) obtains the highest accuracy of 99.24% compared to other individual algorithms. MATLAB 2014 is utilized to implement the brain tumor classification and segmentation algorithms in this research effort.

Brain tumor detection and analysis are necessary for any indicative system and have testified that exhaustive research and procedural development over time. This work needs to implement an effective automated system to improve the accuracy of tumor detection. Various segmentation algorithms have been developed to achieve and enhance the accuracy of brain tumor classification. Brain image segmentation has been recognized as a complex and challenging area in medical image processing. This paper proposes a novel automated scheme for detection and classification. The proposed method is divided into various categories: MRI image preprocessing, image segmentation, feature extraction, and image classification. The image preprocessing step is performed using an adaptive filter to remove the noise of the MRI image. Image segmentation is performed using the improved K-means clustering (IKMC) algorithm, and the gray level co-occurrence matrix (GLCM) is used for feature extraction to extract features. After extracting features from MRI images, we used a deep learning model to classify the types of images such as gliomas, meningiomas, non-tumors, and pituitary tumors. The classification process was performed using recurrent convolutional neural networks (RCNN). The proposed method provides better results for classifying brain images from a given input dataset. The experiments were conducted on the Kaggle dataset with 394 testing sets and 2870 training set MRI images. The results illustrate that the proposed method achieves a higher performance than previous methods. Finally, the proposed RCNN method is compared with the current classification methods of BP, U-Net, and RCNN. The proposed classifier obtained 95.17% accuracy in classifying brain tumor tissues from MRI images.

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause.The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.